Ocean and Ecosystem Science Seminar Series | BIO | 2019-11-06
The greatest value of a picture is when it forces us to notice
what we never expected to see.
- Tukey (1977)
Simplified workflow:
Data
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Models
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Discuss
library(Rstrap)
## Load survey data
load("converted_set_details_2017-04-27.Rdata")
load("converted_length-frequency_data_2017-04-27.Rdata")
load("age-growth_data_2017-04-27.Rdata")
## 3NO Witch Flounder
witch <- strat.fun(setdet = setdet, data.series = "Campelen",
species = 890, survey.year = 1995:2012,
season = "spring", NAFOdiv = c("3N", "3O"))
## 3K Roughhead Grenadier (length disaggregated)
grenadier <- strat.fun(setdet = setdet, lf = lf, data.series = "Campelen",
program = "strat2 & strat1", species = 474, survey.year = 2012,
season = "fall", NAFOdiv = "3K", strat = 622:654, sex = "unsexed",
length.group = 0.5, group.by = "length")
## 2J3KL Greenland Halibut (length and age disaggregated)
turbot <- strat.fun(setdet = setdet, lf = lf, ag = ag, data.series = "Campelen",
program = "strat2 & strat1", species = 892, survey.year = 1998:2012,
season = "fall", NAFOdiv = c("2J", "3K", "3L"),
sex = c("male","female","unsexed"), length.group = 1,
group.by = "length & age")
## 3Ps Atlantic Cod (length and age disaggregated)
cod <- strat.fun(setdet = setdet, lf = lf, ag = ag, data.series = "Campelen",
program = "strat2 & strat1", species = 438, survey.year = 1998:2012,
season = "spring", NAFOdiv = "3P", sex = c("male","female","unsexed"),
length.group = 1, group.by = "length & age")
shiny, flexdashboard, plotly, and crosstalk packages to dynamically run and explore RStrap results
Photo: John Brattey
shiny, shinydashboard, and leaflet packages to dynamically explore this large data-setProcess equations
Stochastic cohort model with a plus group to model the unobserved states: \[\log(N_{a,y}) = \left\{\begin{matrix}
\log(N_{a-1,y-1}) - Z_{a-1,y-1} + \delta_{a,y}, & a < A \\
\log\{N_{a-1,y-1}\exp(-Z_{a-1,y-1}) + N_{a,y-1}\exp(-Z_{a,y-1})\} + \delta_{a,y}, & a = A.
\end{matrix}\right. \]
The ages are 1-10+ and years are 1975-2015. \(Z_{a,y} = F_{a,y} + M_{a,y}\), where \(M_{a,y} = 0.2\) is the base case assumption.
Recruitments, \(N_{1,1}, ... N_{1,Y}\), are treated as uncorrelated lognormal random variables \[\log(N_{1,y}) \overset{iid}\sim N(r, \sigma_{r}^2).\]
Catches are modeled using the Baranov catch equation, \[C_{a,y} = N_{a,y}\{1 - \exp(-Z_{a,y})\}F_{a,y}/Z_{a,y}.\]
Fishing moralities are modeled as a stochastic process, with \[Cov\{\log(F_{a,y}),\log(F_{a-j,y-k})\} = \frac{\sigma_{F}^2 \varphi_{F,a}^j \varphi_{F,y}^k}{(1-\varphi_{F,a}^2)(1-\varphi_{F,y}^2)}.\]
Observation equations
The model predicted catch for survey \(s\) is \[\log(I_{s,a,y}) = \log(q_{s,a}) + \log(N_{a,y}) - t_{s,y}Z_{a,y} + \varepsilon_{s,a,y}, ~~ \varepsilon_{s,a,y} \overset{iid}\sim N(0, \sigma_{s,G(a)}^2).\]
Survey variance was split out and self-weighted by age groups 1-3, 4-7, and 8-10+
Total catch and age compositions were treated separately. Total catch was modeled as lognormal, \[\log(C_{obs,y}) = \log(C_{y}) + \varepsilon_{C,y}, ~~ \varepsilon_{C,y} \overset{iid}\sim N(0, \sigma_{C}^2)\] Age compositions were modeled as multiplicative logistic normal with a censored component for zero’s
Code
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Plots & Tables
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Documentation
flexdashboard, plotly, and crosstalk packages to explore and communicate results from NCAMCode
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HTML
Data
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Code
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Models
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Code
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Document
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Discuss
We thank the numerous colleagues and participants of various stakeholder and stock assessment meetings who encouraged us to further develop these interactive visualization tools, and especially those who took the time to make suggestions on how to make them more accessible and useful.